3D Human Pose Estimation in RGBD Images for Robotic Task Learning

Abstract

We propose an approach to estimate 3D human
pose in real world units from a single RGBD image and show
that it exceeds performance of monocular 3D pose estimation
approaches from color as well as pose estimation exclusively
from depth. Our approach builds on robust human keypoint
detectors for color images and incorporates depth for lifting into
3D. We combine the system with our learning from demonstration framework to instruct a service robot without the need of
markers. Experiments in real world settings demonstrate that
our approach enables a PR2 robot to imitate manipulation
actions observed from a human teacher.